def render_fairness_indicator(
eval_result: model_eval_lib.EvalResult,
slicing_column: Optional[Text] = None,
slicing_spec: Optional[slicer.SingleSliceSpec] = None,
output_name: Text = '',
multi_class_key: Text = '',
event_handlers: Optional[Dict[Text, Callable[..., Any]]] = None,
) -> Optional[Any]:
"""Renders the Fairness Indicator view.
Args:
eval_result: An tfma.EvalResult.
slicing_column: The slicing column to to filter results. If both
slicing_column and slicing_spec are None, show all eval results.
slicing_spec: The slicing spec to filter results. If both slicing_column and
slicing_spec are None, show all eval results.
output_name: The output name associated with metric (for multi-output
models).
multi_class_key: The multi-class key associated with metric (for multi-class
models).
event_handlers: The event handler callback.
Returns:
A FairnessIndicatorViewer object if in Jupyter notebook; None if in Colab.
"""
data = convert_eval_result_to_ui_input(eval_result, slicing_column,
slicing_spec, output_name,
multi_class_key)
return visualization.render_fairness_indicator(data, event_handlers)
def render_fairness_indicator(
eval_result: Optional[view_types.EvalResult] = None,
multi_eval_results: Optional[Dict[str, view_types.EvalResult]] = None,
slicing_column: Optional[str] = None,
slicing_spec: Optional[slicer.SingleSliceSpec] = None,
output_name: str = '',
multi_class_key: str = '',
event_handlers: Optional[Dict[str, Callable[..., Any]]] = None,
) -> Optional[Any]:
"""Renders the Fairness Indicator view.
Args:
eval_result: An tfma.EvalResult.
multi_eval_results: A map {string: tfma.EvalResult} of length 2, mapping an
eval or model name to an EvalResult. This can be used for model
comparison.
slicing_column: The slicing column to to filter results. If both
slicing_column and slicing_spec are None, show all eval results. This is
ignored for cross slice comparison based results.
slicing_spec: The slicing spec to filter results. If both slicing_column and
slicing_spec are None, show all eval results. This is ignored for cross
slice comparison based results.
output_name: The output name associated with metric (for multi-output
models).
multi_class_key: The multi-class key associated with metric (for multi-class
models).
event_handlers: The event handler callback.
Returns:
A FairnessIndicatorViewer object if in Jupyter notebook; None if in Colab.
"""
if ((eval_result and multi_eval_results)
or (not eval_result and not multi_eval_results)):
raise ValueError(
'Exactly one of the "eval_result" and "multi_eval_results" '
'parameters must be set.')
if (multi_eval_results and len(multi_eval_results) != 2):
raise ValueError(
'"multi_eval_results" parameter only accepts 2 eval results.')
data = None
multi_data = None
if eval_result:
data = convert_slicing_metrics_to_ui_input(eval_result.slicing_metrics,
slicing_column,
slicing_spec, output_name,
multi_class_key)
else:
multi_data = {}
for eval_name in multi_eval_results:
multi_data[eval_name] = convert_slicing_metrics_to_ui_input(
multi_eval_results[eval_name].slicing_metrics, slicing_column,
slicing_spec, output_name, multi_class_key)
return visualization.render_fairness_indicator(data, multi_data,
event_handlers)
def render_fairness_indicator(
eval_result: Optional[model_eval_lib.EvalResult] = None,
multi_eval_results: Optional[Dict[Text, model_eval_lib.EvalResult]] = None,
slicing_column: Optional[Text] = None,
slicing_spec: Optional[slicer.SingleSliceSpec] = None,
output_name: Text = '',
multi_class_key: Text = '',
event_handlers: Optional[Dict[Text, Callable[..., Any]]] = None,
) -> Optional[Any]:
"""Renders the Fairness Indicator view.
Args:
eval_result: An tfma.EvalResult.
multi_eval_results: A map of {string: tfma.EvalResult}. The key is the eval
or model name.
slicing_column: The slicing column to to filter results. If both
slicing_column and slicing_spec are None, show all eval results.
slicing_spec: The slicing spec to filter results. If both slicing_column and
slicing_spec are None, show all eval results.
output_name: The output name associated with metric (for multi-output
models).
multi_class_key: The multi-class key associated with metric (for multi-class
models).
event_handlers: The event handler callback.
Returns:
A FairnessIndicatorViewer object if in Jupyter notebook; None if in Colab.
"""
if ((eval_result and multi_eval_results)
or (not eval_result and not multi_eval_results)):
raise ValueError(
'Exactly one of the "eval_result" and "multi_eval_results" '
'parameters must be set.')
if (multi_eval_results and len(multi_eval_results) != 2):
raise ValueError(
'"multi_eval_results" parameter only accept 2 eval results.')
data = None
multi_data = None
if eval_result:
data = convert_eval_result_to_ui_input(eval_result, slicing_column,
slicing_spec, output_name,
multi_class_key)
else:
multi_data = {}
for eval_name in multi_eval_results:
multi_data[eval_name] = convert_eval_result_to_ui_input(
multi_eval_results[eval_name], slicing_column, slicing_spec,
output_name, multi_class_key)
return visualization.render_fairness_indicator(data, multi_data,
event_handlers)